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Adversarial Reinforcement Learning Framework for Benchmarking Collision Avoidance Mechanisms in Autonomous Vehicles
Vahid Behzadan, Arslan Munir
TL;DR
Current benchmarks can inadequately compare increasingly complex collision-avoidance methods and may miss critical unsafe states. The paper proposes deep-RL adversaries for worst-case benchmarking; in its case study, AVD was more resilient to direct collisions than AVM in the selected environment.
Problem
Existing benchmarks struggle to compare complex collision-avoidance methods and comprehensively explore mechanism-specific unsafe states.
Source: §1 · p. 1Method
The framework trains deep-RL adversarial agents to target unsafe states and benchmarks collision-avoidance mechanisms under worst-case interaction.
Source: §1 · pp. 1–2Results
AVM converged in 473 versus AVD’s 889 episodes, but AVD was more resilient to direct collisions in the selected environment.
Source: §3 · p. 4Takeaways & Limitations
The case study demonstrates a reliability comparison of two collision-avoidance mechanisms under intentional direct-collision attempts.
Source: §1 · p. 2, §3 · p. 4Valid comparisons require the same deep-RL model architecture and configuration, and repeated measurements are necessary given deep-RL and simulation stochasticity.
Source: §2.3 · p. 3, §2.4 · p. 4
Abstract
from arXiv · showhide
With the rapidly growing interest in autonomous navigation, the body of research on motion planning and collision avoidance techniques has enjoyed an accelerating rate of novel proposals and developments. However, the complexity of new techniques and their safety requirements render the bulk of current benchmarking frameworks inadequate, thus leaving the need for efficient comparison techniques unanswered. This work proposes a novel framework based on deep reinforcement learning for benchmarking the behavior of collision avoidance mechanisms under the worst-case scenario of dealing with an optimal adversarial agent, trained to drive the system into unsafe states. We describe the architecture and flow of this framework as a benchmarking solution, and demonstrate its efficacy via a practical case study of comparing the reliability of two collision avoidance mechanisms in response to intentional collision attempts.
1 INTRODUCTION
Reliable motion planning and collision avoidance remain difficult to assure as methods grow more complex, while current benchmarks struggle to compare them robustly. This paper proposes deep-RL adversaries for worst-case benchmarking.
1 INTRODUCTION
- Safety assurance is increasingly difficult for complex collision-avoidance methods, spanning control-theoretic, optimal-control, potential-field, rule-based, imitation-learning, and deep-RL approaches.The cited prior work includes for motion-planning approaches, for imitation learning, and [5] for deep RL.Source: §1 · p. 1
- Case-specific assumptions and ad hoc verification make quantitative comparison difficult, while existing benchmarks can miss mechanism-specific unsafe states and lack concrete safety-boundary guarantees.Randomized or scenario-based obstacle generation may miss critical Inevitable Collision States; reachability analysis is computationally expensive and also fails to provide concrete guarantees on critical boundaries.Source: §1 · p. 1
- The framework uses deep RL to train an optimal adversarial agent to drive an AV into Inevitable Collision States or other unsafe states.Depending on the analysis objective, the adversary may attempt direct collisions or manipulate the AV's trajectory to induce indirect collisions.Source: §1 · pp. 1–2
- The contributions include a worst-case benchmarking process flow, deep-RL adaptation beyond random or scenario-based exploration, standardized comparison metrics, and a practical reliability case study.The framework is proposed to apply independently of algorithm complexity, stochasticity, and adaptive dynamics.Source: §1 · p. 2
2 PROPOSED ARCHITECTURE
The framework uses deep RL to train adversarial navigation policies for worst-case benchmarking, with reliability assessed through training, test-time, and environment-configuration metrics.
2 PROPOSED ARCHITECTURE
- The framework combines a simulation environment, adversarial objectives, a deep RL algorithm, and quantitative metrics to benchmark collision-avoidance behavior in worst-case scenarios.Adversarial policies seek intentional direct or indirect collisions, unlike AV policies that seek collision avoidance.Source: §2 · p. 2
2.1 Simulation Environment
- The framework is designed to work across simulation platforms if they provide state monitoring and agent control, plus external adjustment of simulation flow and speed.The speed adjustment is intended to eliminate effects of training and computational lags on simulation flow.Source: §2.1 · p. 2
2.2 Objectives
- Three adversarial objectives are direct collision with the test vehicle, inducing its collision with another object, and manipulating its trajectory.The framework allows arbitrary reward functions for these objectives, but offers a best-effort solution and guarantees convergence only when the objective is reachable.Source: §2.2 · p. 3
2.3 Deep RL Model
- The framework permits arbitrary deep RL model types and configurations, but valid comparisons require the same architecture and configuration across the comparison domain.Continuous-control algorithms are relevant to driving, while exploration mechanisms and model inputs inform architecture choices.Source: §2.3 · p. 3
2.4 Metrics
- Benchmarking metrics cover training, test-time behavior, and initial environment configurations, with repeated measurements needed to account for stochasticity.Examples include iterations to convergence, time or distance to damage, damage to each vehicle, and the maximum number of agents that can safely coexist with the test vehicle.Source: §2.4 · pp. 3–4
3 CASE STUDY
The case study compares two ground AVs against adversaries trained for direct collisions and finds AVD more resilient than AVM in the selected environment.
3 CASE STUDY
- The case study compares AVD, using policies from deep RL [13], with AVM, the Olethros bot using model-predictive control [11], in TORCS.The experiment includes the two AVs, an adversarial agent, and default TORCS bots.Source: §3 · p. 4, Table 1 · p. 4
- The AVD implementation adds opponent-distance vectors to the prior model’s state parameters for more accurate positioning of other agents.Source: §3 · p. 4
- AVM converged faster (473 vs 889 episodes) and had lower return; AVD resisted direct collisions longer, though authors judged it less robust than AVM in this environment.The results are averaged over 100 runs.Source: Table 2 · p. 4, §3 · p. 4
4 SUMMARY AND FUTURE DIRECTIONS
The paper presents adversarial deep-RL benchmarking for AV motion-planning and collision-avoidance reliability, with adaptive probing and worst-case baselines. It identifies reference benchmarks and adversarial training as future research directions.
4 SUMMARY AND FUTURE DIRECTIONS
- The framework uses adversarial deep reinforcement learning to measure reliability and offers adaptive probing, customizable exploration, and worst-case baselines for comparing heterogeneous systems.Source: §4 · p. 4
- A next step is applying the framework to prominent motion-planning and collision-avoidance techniques to create reference benchmarks.Source: §4 · p. 4
- The authors propose investigating adversarial-policy and collision-avoidance-model training together as a possible mitigation and defense avenue.Source: §4 · p. 4